Python - 隊列，管理器和多處理的奇怪行為

Question

在使用multiprocessing模塊同時下載我的寵物項目時，我遇到了一個涉及由multiprocessing.Manager()對象生成的Queue對象的奇怪行為。

根據我將Queue對象（通過Manager生成）放在另一個Queue對象（也是通過Manager生成）中的方式，我得到了一個不同的行為，據我理解，同樣的事情。 這是一個最小的工作示例：

import multiprocessing
import Queue

def work(inbound_queue, keep_going):
    while keep_going.value == 1:
        try:
            outbound_queue = inbound_queue.get(False) # this fails in case 3
            #do some work
            outbound_queue.put("work done!")
        except Queue.Empty:
            pass #this is busy wait of course, it's just an example

class Weird:
    def __init__(self):
        self.manager = multiprocessing.Manager()
        self.queue = self.manager.Queue()
        self.keep_going = multiprocessing.Value("i", 1)
        self.worker = multiprocessing.Process(target = work, args = (self.queue, self.keep_going))
        self.worker.start()
    def stop(self): #close and join the second process
        self.keep_going.value = 0
        self.worker.join()
    def queueFromOutside(self, q):
        self.queue.put(q)
        return q
    def queueFromNewManager(self):
        q = multiprocessing.Manager().Queue()
        self.queue.put(q)
        return q
    def queueFromOwnManager(self):
        q = self.manager.Queue()
        self.queue.put(q)
        return q

if __name__ == '__main__':
    instance = Weird()
    # CASE 1
    queue = multiprocessing.Manager().Queue()
    q1 = instance.queueFromOutside(queue) # Works fine
    print "1: ", q1.get()

    # CASE 2
    q2 = instance.queueFromNewManager()   # Works fine
    print "2: ", q2.get()

    # CASE 3
    q3 = instance.queueFromOwnManager()   # Error
    print "3: ", q3.get()

    instance.stop() #sadly never called :(

及其輸出（python 2.7.10 x86，windows）。

主要輸出：

1:  work done!
2:  work done!
3:

然后工作進程崩潰，讓q3.get（）掛起。

工作流程的輸出：

Process Process-2:
Traceback (most recent call last):
  File "C:\Python27\lib\multiprocessing\process.py", line 258, in _bootstrap
    self.run()
  File "C:\Python27\lib\multiprocessing\process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "J:\Dropbox\Python\queues2.py", line 7, in work
    outbound_queue = inbound_queue.get(False) # this fails in case 3
  File "<string>", line 2, in get
  File "C:\Python27\lib\multiprocessing\managers.py", line 774, in _callmethod
    raise convert_to_error(kind, result)
RemoteError:
---------------------------------------------------------------------------
Unserializable message: ('#RETURN', <Queue.Queue instance at 0x025A22B0>)
---------------------------------------------------------------------------

所以問題是：為什么第三種情況導致RemoteError ？

提供的示例與實際項目中的代碼結構不相似，但我確實將隊列發送到正在運行的進程，如果我使用方法＃1和＃2進行處理，它的工作正常。 但是使用方法＃3會很好，因為它可以省去每次獲取Manager的麻煩，這可能會花費很長的時間（我現在正在使用的機器上大約100毫秒）。

這個問題出於好奇，因為我還在學習multiprocessing模塊中所有很酷的東西。

更新，嘗試澄清問題：在案例3（ queueFromOwnManager ）中，為什么self.manager.Queue()創建一個曾經放入self.queue的隊列，在創建隊列時無法使用self.queue.get()檢索with multiprocessing.Manager().Queue()可以檢索multiprocessing.Manager().Queue()嗎？ 執行3個案件的順序無關緊要。 理想情況下，在3個示例中的3個方法調用中的任何一個之前和之后， instance.queue將為空。

更新2：使得exaple更像我在代碼中實際做的那樣

Answer 1

更新了答案，我在main中添加了代碼，用於從列表ls中填充和打印放置在隊列self.queue中的項目。 我還添加了一個語句，可用於從外部函數中的ls中檢索self.queue中的項目。

import multiprocessing
import Queue

def work(inbound_queue, keep_going):
    while keep_going.value == 1:
        try:
            pass
            #outbound_queue = inbound_queue.get(False) # this fails in case 3 #<--- an error here, wherefrom does it get truthvalues?
            #do some work
            #outbound_queue.put("work done!")
        except: #Queue.Empty:                            <--- self.queue.Empty(): when instantiated below
            pass #this is busy wait of course, it's just an example

class Weird:
    def __init__(self):
        self.manager = multiprocessing.Manager()
        self.queue = self.manager.Queue()
        self.queue2 = multiprocessing.Manager().Queue()
        self.keep_going = multiprocessing.Value("i", 1)
        self.worker = multiprocessing.Process(target = work, args = (self.queue, self.keep_going))
        self.worker.start()
    def stop(self): #close and join the second process
        self.keep_going.value = 0
        #self.worker.join()
    def queueFromOutside(self, q):

        ls = [1,2,3,4,5]
        # populate self.queue with elements from list ls
        for i in ls:
          self.queue.put(i)
        return self.queue
    def queueFromNewManager(self):
         #q = multiprocessing.Manager().Queue()  <---- note that you state that manager and not "queue"
                                                      # is the name of the queue in this step,
                                                      # therefor errormsg, at this step manager
                                                     # is empty and self.queue has been given ls


        ls = [5,6,7,8]
        # populate self.queue with elements from list ls
        for i in ls:

          self.queue.put(i)
        return self.queue


    def queueFromOwnManager(self):
        q = self.manager.Queue()


        ls = [5,6,7,8]
        # populate self.queue with elements from list ls
        for i in ls:
          self.queue.put(i)
        return self.queue

    def wait_completion(self):     #<---- function that waits until tasks are done, and joins all
                                            # tasks as a last step, check docs how to add tasks and data to manager
        """Wait for completion of all the tasks in the queue"""
        self.tasks.join()

if __name__ == '__main__':
    instance = Weird()
    # CASE 1

    q1 = instance.queueFromOutside(instance.queue2) # Works fine
    print "1: ", q1.get()

    #this code gets data from instance.queue in external functions
    if not instance.queue.empty():

        item = instance.queue.get(True)
        print item,"item"

    # CASE 2
    q2 = instance.queueFromNewManager()   # Works fine
    print "2: ", q2.get()

    # CASE 3
    q3 = instance.queueFromOwnManager()   # Error
    print "3: ", q3.get()

    instance.stop() #sadly never called :(

    # when all tasks done  wait_completion(self) can be printed here to join all tasks